{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Disaggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import time\n",
    "from matplotlib import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from six import iteritems\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "rcParams['figure.figsize'] = (13, 6)\n",
    "\n",
    "from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore\n",
    "from nilmtk.disaggregate import CombinatorialOptimisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividing data into train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = DataSet('/data/REDD/redd.h5')\n",
    "test = DataSet('/data/REDD/redd.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us use building 1 for demo purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "building = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split data at April 30th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.set_window(end=\"30-4-2011\")\n",
    "test.set_window(start=\"30-4-2011\")\n",
    "\n",
    "\n",
    "train_elec = train.buildings[1].elec\n",
    "test_elec = test.buildings[1].elec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27fe8e66320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wyFS4XFZDKkTCyLRp03A6nXTr1o327dtz++23h7pJchFlKkRExFcYBBWKKETCy+zZs0Pd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\njDHGGGOMMZPy/wEAHGDAiBDV3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27fe8e500b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_elec.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27fe9b6bda0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vSVlJIVdhvwD5OqSYeIMKnkUiIqLwAk9SMahI1GQ9FU0TEQFwP4B1qvo114+e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\nGIZhGIZhGIbhCzMqDMMwDMMwDMPwxf8HBKaPhOZsqREAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27fe9b5ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_elec.mains().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REDD data set has got appliance level data sampled every 3 or 4 seconds and mains data sampled every 1 second. Let us verify the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fridge_meter = train_elec['fridge']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fridge_df = next(fridge_meter.load())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>physical_quantity</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:13-04:00</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:16-04:00</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:20-04:00</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:23-04:00</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:26-04:00</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "physical_quantity          power\n",
       "type                      active\n",
       "2011-04-18 09:22:13-04:00    6.0\n",
       "2011-04-18 09:22:16-04:00    6.0\n",
       "2011-04-18 09:22:20-04:00    6.0\n",
       "2011-04-18 09:22:23-04:00    6.0\n",
       "2011-04-18 09:22:26-04:00    6.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mains = train_elec.mains()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    }
   ],
   "source": [
    "mains_df = next(mains.load())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>physical_quantity</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>apparent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:09-04:00</th>\n",
       "      <td>342.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:10-04:00</th>\n",
       "      <td>344.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:11-04:00</th>\n",
       "      <td>345.140015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:12-04:00</th>\n",
       "      <td>341.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:13-04:00</th>\n",
       "      <td>341.029999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "physical_quantity               power\n",
       "type                         apparent\n",
       "2011-04-18 09:22:09-04:00  342.820007\n",
       "2011-04-18 09:22:10-04:00  344.559998\n",
       "2011-04-18 09:22:11-04:00  345.140015\n",
       "2011-04-18 09:22:12-04:00  341.679993\n",
       "2011-04-18 09:22:13-04:00  341.029999"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mains_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since, both of these are sampled at different frequencies, we will downsample both to 1 minute resolution. We will also select the top-5 appliances in terms of energy consumption and use them for training our FHMM and CO models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting top-5 appliances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/16 ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nilmtk/nilmtk/stats/totalenergyresults.py:41: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
      "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  return self._data.fillna(0).convert_objects()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/16 MeterGroup(meters==19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])e=1)])ce=1)])\n",
      "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "16/16 MeterGroup(meters= for ElecMeterID(instance=4, building=1, dataset='REDD') ...   \n",
      "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "Calculating total_energy for ElecMeterID(instance=20, building=1, dataset='REDD') ...   "
     ]
    }
   ],
   "source": [
    "top_5_train_elec = train_elec.submeters().select_top_k(k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_5_train_elec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and disaggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FHMM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model for submeter 'ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])'\n",
      "Training model for submeter 'ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])'\n",
      "Training model for submeter 'ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])'\n",
      "Training model for submeter 'ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])'\n",
      "Training model for submeter 'ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])'\n",
      "Runtime = 2.76029896736145 seconds.\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "from nilmtk.disaggregate import fhmm_exact\n",
    "fhmm = fhmm_exact.FHMM()\n",
    "# Note that we have given the sample period to downsample the data to 1 minute. \n",
    "# If instead of top_5 we wanted to train on all appliance, we would write \n",
    "# fhmm.train(train_elec, sample_period=60)\n",
    "fhmm.train(top_5_train_elec, sample_period=60)\n",
    "end = time.time()\n",
    "print(\"Runtime =\", end-start, \"seconds.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    }
   ],
   "source": [
    "pred = {}\n",
    "gt= {}\n",
    "\n",
    "for i, chunk in enumerate(test_elec.mains().load(sample_period=60)):\n",
    "    chunk_drop_na = chunk.dropna()\n",
    "    pred[i] = fhmm.disaggregate_chunk(chunk_drop_na)\n",
    "    gt[i]={}\n",
    "    \n",
    "    for meter in test_elec.submeters().meters:\n",
    "        # Only use the meters that we trained on (this saves time!)    \n",
    "        gt[i][meter] = next(meter.load(sample_period=60))\n",
    "    gt[i] = pd.DataFrame({k:v.squeeze() for k,v in iteritems(gt[i])}, index=next(iter(gt[i].values())).index).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If everything can fit in memory\n",
    "gt_overall = pd.concat(gt)\n",
    "gt_overall.index = gt_overall.index.droplevel()\n",
    "pred_overall = pd.concat(pred)\n",
    "pred_overall.index = pred_overall.index.droplevel()\n",
    "\n",
    "# Having the same order of columns\n",
    "gt_overall = gt_overall[pred_overall.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Intersection of index\n",
    "gt_index_utc = gt_overall.index.tz_convert(\"UTC\")\n",
    "pred_index_utc = pred_overall.index.tz_convert(\"UTC\")\n",
    "common_index_utc = gt_index_utc.intersection(pred_index_utc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_timezone = train.metadata['timezone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_index_local = common_index_utc.tz_convert(local_timezone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_overall = gt_overall.ix[common_index_local]\n",
    "pred_overall = pred_overall.ix[common_index_local]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])</th>\n",
       "      <th>ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])</th>\n",
       "      <th>ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])</th>\n",
       "      <th>ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])</th>\n",
       "      <th>ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])</th>\n",
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      "text/plain": [
       "                           ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])  \\\n",
       "2011-04-30 00:01:00-04:00                                             1131.0                                                           \n",
       "2011-04-30 00:02:00-04:00                                             1128.0                                                           \n",
       "2011-04-30 00:03:00-04:00                                             1133.0                                                           \n",
       "2011-04-30 00:04:00-04:00                                             1129.0                                                           \n",
       "2011-04-30 00:05:00-04:00                                             1130.0                                                           \n",
       "\n",
       "                           ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])  \\\n",
       "2011-04-30 00:01:00-04:00                                               76.0                                                     \n",
       "2011-04-30 00:02:00-04:00                                               76.0                                                     \n",
       "2011-04-30 00:03:00-04:00                                               76.0                                                     \n",
       "2011-04-30 00:04:00-04:00                                               77.0                                                     \n",
       "2011-04-30 00:05:00-04:00                                               76.0                                                     \n",
       "\n",
       "                           ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])  \\\n",
       "2011-04-30 00:01:00-04:00                                                6.0                                                      \n",
       "2011-04-30 00:02:00-04:00                                                6.0                                                      \n",
       "2011-04-30 00:03:00-04:00                                                6.0                                                      \n",
       "2011-04-30 00:04:00-04:00                                                6.0                                                      \n",
       "2011-04-30 00:05:00-04:00                                                6.0                                                      \n",
       "\n",
       "                           ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])  \\\n",
       "2011-04-30 00:01:00-04:00                                               31.0                                                       \n",
       "2011-04-30 00:02:00-04:00                                               29.0                                                       \n",
       "2011-04-30 00:03:00-04:00                                               28.0                                                       \n",
       "2011-04-30 00:04:00-04:00                                               28.0                                                       \n",
       "2011-04-30 00:05:00-04:00                                               28.0                                                       \n",
       "\n",
       "                           ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])  \n",
       "2011-04-30 00:01:00-04:00                                                4.0                                                         \n",
       "2011-04-30 00:02:00-04:00                                                4.0                                                         \n",
       "2011-04-30 00:03:00-04:00                                                4.0                                                         \n",
       "2011-04-30 00:04:00-04:00                                                4.0                                                         \n",
       "2011-04-30 00:05:00-04:00                                                4.0                                                         "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt_overall.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using prettier names!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "appliance_labels = [m.label() for m in gt_overall.columns.values]\n",
    "gt_overall.columns = appliance_labels\n",
    "pred_overall.columns = appliance_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Dish washer</th>\n",
       "      <th>Light</th>\n",
       "      <th>Fridge</th>\n",
       "      <th>Sockets</th>\n",
       "      <th>Microwave</th>\n",
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       "                           Dish washer  Light  Fridge  Sockets  Microwave\n",
       "2011-04-30 00:01:00-04:00          0.0    2.0   194.0     22.0     1098.0\n",
       "2011-04-30 00:02:00-04:00          0.0    2.0   194.0     22.0     1098.0\n",
       "2011-04-30 00:03:00-04:00          0.0    2.0   194.0     22.0     1098.0\n",
       "2011-04-30 00:04:00-04:00          0.0    2.0   194.0     22.0     1098.0\n",
       "2011-04-30 00:05:00-04:00          0.0    2.0   194.0     22.0     1098.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_overall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27fea0380b8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
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F5KgFrk6VjycHEj/j5euB2K/JvINtRgsZcTP4AwajAJEcqIo3OysHatkIYVkR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\nsczw/wHSJ2R9ww4BvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27fea038b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_overall['Fridge'].head(1000).plot(label=\"Pred\")\n",
    "gt_overall['Fridge'].head(1000).plot(label=\"GT\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rms_error = {}\n",
    "for appliance in gt_overall.columns:\n",
    "    rms_error[appliance] = np.sqrt(mean_squared_error(gt_overall[appliance], pred_overall[appliance]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dish washer    182.148235\n",
       "Fridge         174.803628\n",
       "Light           92.915403\n",
       "Microwave      177.634751\n",
       "Sockets         46.909119\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(rms_error)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
